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import streamlit as st |
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from annotated_text import annotated_text |
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import pandas as pd |
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import yaml |
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import os |
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from src.negation import * |
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from src.app_utils import * |
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from src.inference import inference |
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from src.trainers import eval_spacy |
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with open('./st_config.yaml', "r") as yamlfile: |
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args = yaml.load(yamlfile, Loader=yaml.FullLoader) |
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if args['model_dir'] is None: |
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model_names_dir = [] |
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elif os.path.exists(args['model_dir']): |
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model_names_dir = os.listdir(args['model_dir']) |
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else: |
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model_names_dir = [] |
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model_names = model_names_dir + args['default_models'] if args['default_models'] is not None else model_names_dir |
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st.title('Radiology NER') |
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st.markdown('This app is to experiment on using NER to extract span of text from radiological notes that will to indicate the current condition of the patient. The targeted extraction includes \n 1) the symptoms of the disease \n 2) the location of the organs affected the symptops \n 3) and the progress of the disease. \nen_ner_bc5cdr_md is the base model already trained to detect diseases and chemicals. en_Radiology_ner_bc5cdr_md is fine tuned on the base model with additional entities to indicate "Existence or Worsening" of symptoms and "Absence or recovering" of symptoms. This will help practitioners to quickly note the key words in the clinical report as well as the tabulated result can be use for analysis for other downstream task') |
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model_name= st.sidebar.selectbox("Select a model", options=model_names) |
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print(model_name) |
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if len(model_names) > 0: |
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models = load_models(model_names,args, model_names_dir) |
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print(models) |
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selected_model = models[model_name] |
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print(selected_model) |
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st.sidebar.markdown('###') |
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if args['examples'] is not None: |
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chosen_note = st.sidebar.selectbox("Select an example text", options=args['examples'].keys()) |
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else: |
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chosen_note = None |
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if chosen_note == "radiology_eval_dataset": |
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text_input = pd.read_csv("./eval_35.csv", converters={'entities': ast.literal_eval}) |
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text_input = text_input.to_dict('records') |
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if len(model_names) > 0: |
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ents_available = selected_model.get_pipe('ner').labels |
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print(ents_available) |
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ent_colors_map = dict(map(lambda i,j : (i,j) , ents_available,args['colors_palette'][:len(ents_available)])) |
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if chosen_note != "radiology_eval_dataset": |
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text_input = st.text_area("Type notes in the box below", |
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value=args['examples'][chosen_note] if args['examples'] is not None else '') |
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st.markdown("---") |
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st.sidebar.info('For csv & json files, name the text columns to be infered as "text". Annotated labels as "entities" Format of json text as below') |
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st.sidebar.json([{"text":"example","entities":[[5,6,"do"],[8,11,"dx"]]},{"text":"example2","entities":[[5,6,"do"],[8,11,"dx"]]}],expanded=False) |
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uploaded_file = st.sidebar.file_uploader("Upload a file", type=["csv","json","pdf", "txt"]) |
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text_input = process_files(uploaded_file, text_input) |
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selected_entities = st.sidebar.multiselect( |
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"Select the entities you want to view", |
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options=ents_available if len(model_names)> 0 else [], |
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default=ents_available if len(model_names)> 0 else [], |
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) |
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if (len(text_input)> 1) & (isinstance(text_input,(list,dict))): |
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sample = st.slider('Select Example', min_value=1, max_value=len(text_input)) |
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else: |
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sample = None |
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if len(model_names)>0: |
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infer_input = text_input[sample-1]["text"] if sample is not None else text_input |
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doc = selected_model(infer_input) |
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textcol_negate, textcol_compare = st.columns([1, 1]) |
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negate = textcol_negate.checkbox('Check for Negation') |
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if (isinstance(text_input,(dict,list))): |
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if 'entities' in text_input[0].keys(): |
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state_compare = False |
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compare = textcol_compare.checkbox('Compare between predictions and labels',disabled=state_compare) |
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else: |
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state_compare, compare = True, False |
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else: |
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state_compare, compare = True, False |
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if negate: |
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neg_ent = {"ent_types":list(selected_model.get_pipe('ner').labels)} |
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neg = negation(selected_model, neg_ent) |
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doc = infer_negation(neg,selected_model,infer_input,doc) |
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selected_entities += ['NEG'] |
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ent_colors_map.update({'NEG': '#C7C7C7'}) |
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if compare & (isinstance(text_input,(dict,list))): |
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infer_input = text_input[sample-1] |
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tokens_compare = process_text_compare(infer_input,selected_entities,colors=ent_colors_map) |
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tokens = process_text(doc, selected_entities,colors=ent_colors_map) |
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st.markdown('##') |
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st.markdown('#### Predictions') |
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annotated_text(*tokens) |
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if compare & (isinstance(text_input,(dict,list))): |
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st.markdown('#### Labels') |
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annotated_text(*tokens_compare) |
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st.markdown("---") |
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data = pd.DataFrame.from_dict([{'label': entity.label_, 'text': entity.text, 'start': entity.start, 'end': entity.end} \ |
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for entity in doc.ents]) |
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if data.shape[1]>0: |
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st.table(data['label'].value_counts()) |
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myexpander = st.expander('Details on text') |
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myexpander.table(data) |
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infer_whole_dataset = st.checkbox('Inference on whole dataset') |
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if (isinstance(text_input,(dict,list))) & (infer_whole_dataset): |
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texts = [] |
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for text in text_input: |
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texts.append(text['text']) |
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st.markdown('### Prediction on whole dataset') |
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inference_data = inference(selected_model,texts) |
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if negate: |
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neg_ent = {"ent_types":list(selected_model.get_pipe('ner').labels)} |
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neg = negation(selected_model, neg_ent) |
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docs = selected_model.pipe(texts,batch_size=8) |
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records = [] |
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for no,doc in enumerate(docs): |
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doc = infer_negation(neg,selected_model,texts[no],doc) |
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if len(doc.ents)>0: |
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records.append([{'id':no+1,'text':doc.text,'span': entity.text, |
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'entity': entity.label_, 'start': entity.start, 'end': entity.end} |
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for entity in doc.ents]) |
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else: |
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records.append([{'id':no+1,'text':doc.text,'span': None, |
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'entity': None, 'start':None, 'end': None}]) |
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inference_data = pd.DataFrame.from_dict(sum(records,[])).set_index(['text','id']) |
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st.download_button( |
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label="Download Prediction as CSV", |
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data=inference_data.to_csv().encode('utf-8'), |
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file_name='inference_data.csv', |
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mime='text/csv', |
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) |
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report_expander = st.expander('Report on Evaluation Results') |
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results_metrics = eval_spacy(selected_model,text_input) |
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overall_score = pd.DataFrame.from_dict({'Type':['Overall'],'Precision': [results_metrics['ents_p']], |
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'Recall': [results_metrics['ents_r']], |
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'F1': [results_metrics['ents_f']]}) |
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overall_score = overall_score.set_index('Type') |
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entities_score = pd.DataFrame.from_dict(results_metrics['ents_per_type']).T |
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entities_score = entities_score.rename(columns={'p':'Precision','r':'Recall','f':'F1'}) |
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report_expander.table(overall_score) |
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report_expander.table(entities_score) |
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df_expander = st.expander('Inference Table') |
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df_expander.write(inference_data.to_html(), unsafe_allow_html=True) |
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